Bayesian Forecasting of US Growth using Basic Time Varying Parameter Models and Expectations Data

__Abstract__ Time varying patterns in US growth are analyzed using various univariate model structures, starting from a naive model structure where all features change every period to a model where the slow variation in the conditional mean and changes in the conditional variance are specified together with their interaction, including survey data on expected growth in order to strengthen the information in the model. Use is made of a simulation based Bayesian inferential method to determine the forecasting performance of the various model specifications. The extension of a basic growth model with a constant mean to models including time variation in the mean and variance requires careful investigation of possible identification issues of the parameters and existence conditions of the posterior under a diffuse prior. The use of diffuse priors leads to a focus on the likelihood fu nction and it enables a researcher and policy adviser to evaluate the scientific information contained in model and data. Empirical results indicate that incorporating time variation in mean growth rates as well as in volatility are important in order to improve for the predictive performances of growth models. Furthermore, using data information on growth expectations is important for forecasting growth in specific periods, such as the the recession periods around 2000s and around 2008

Posterior-Predictive Evidence on US Inflation using Extended Phillips Curve Models with Non-filtered Data

Changing time series properties of US inflation and economic activity, measured as marginal costs, are modeled within a set of extended Phillips Curve (PC) models. It is shown that mechanical removal or modeling of simple low frequency movements in the data may yield poor predictive results which depend on the model specification used. Basic PC models are extended to include structural time series models that describe typical time varying patterns in levels and volatilities. Forward as well as backward looking expectation mechanisms for inflation are incorporated and their relative importance evaluated. Survey data on expected inflation are introduced to strengthen the information in the likelihood. Use is made of simulation based Bayesian techniques for the empirical analysis. No credible evidence is found on endogeneity and long run stability between inflation and marginal costs. Backward-looking inflation appears stronger than forward-looking one. Levels and volatilities of inflation are estimated more precisely using rich PC models. Estimated inflation expectations track nicely the observed long run inflation from the survey data. The extended PC structures compare favorably with existing basic Bayesian Vector Autoregressive and Stochastic Volatility models in terms of fit and prediction. Tails of the complete predictive distributions indicate an increase in the probability of disinflation in recent years

Evidence of vascular endothelial damage in Crimean-Congo hemorrhagic fever

Background: Endothelial infection has an important role in the pathogenesis of Crimean-Congo hemorrhagic fever (CCHF). In this study, we investigated the causes of vascular endothelial damage in patients with CCHF

Evidence of vascular endothelial damage in Crimean-Congo hemorrhagic fever

Background: Endothelial infection has an important role in the pathogenesis of Crimean-Congo hemorrhagic fever (CCHF). In this study, we investigated the causes of vascular endothelial damage in patients with CCHF

Evaluation of primary and accessory respiratory muscles and their influence on exercise capacity and dyspnea in pulmonary arterial hypertension

Background: Skeletal and respiratory muscle disfunction has been described in pulmonary arterial hypertension (PAH), however, involvement of accessory respiratory muscles and their association with symptomatology in PAH is unclear. Objectives: To assess the primary and accessory respiratory muscles and their influence on exercise tolerance and dyspnea. Methods: 27 patients and 27 healthy controls were included. Serratus anterior (SA), pectoralis muscles (PM) and sternocleidomastoid (SCM) muscle strength were evaluated as accessory respiratory muscles, maximal inspiratory (MIP) and expiratory pressures (MEP) as primary respiratory muscles, and quadriceps as peripheral muscle. Exercise capacity was evaluated with 6-min walk test (6MWT), dyspnea with modified Medical Council Research (MMRC) and London Chest Activity of Daily Living (LCADL) scales. Results: All evaluated muscles, except SCM, and 6MWT were decreased in patient group (p < 0.01). SA was the most affected muscle among primary and accessory respiratory muscles (Cohen's-d = 1.35). All evaluated muscles significantly correlated to 6MWT (r = 0.428-0.525). A multivariate model including SA, SCM and MIP was the best model for predicting 6MWT (R = 0.606; R-2 = 0.368; p = 0.013) and SA strength had the most impact on the 6MWT (B =1.242; beta = 0.340). None of the models including respiratory muscles were able to predict dyspnea, however PM and SA strength correlated to LCADL(total) (r =-0.493) and MMRC (r =-0.523), respectively. Conclusion: SCM may be excessively used in PAH since it retains its strength. Considering the relationship of accessory respiratory muscles with exercise tolerance and dyspnea, monitoring the strength of these muscles in the clinical practice may help providing better management for PAH. (C) 2022 Elsevier Inc. All rights reserved